The acquisition of physiological biometric features such as facial and ocular features is a necessary step in most biometric recognition applications (e.g., facial recognition, iris recognition, and the like). Several factors, however, make it difficult for conventional systems to acquire images of the quality necessary to ensure good recognition results, particularly if the biometric features of interest are small (as is the case with the human iris) and/or if the images are collected in an uncontrolled environment. For instance, if the range of the subject is uncertain, or if atmospheric turbulence is present, the image captured may be unusable due to poor focus or blur. In order to prevent defocus and blur, range-finding or turbulence compensation hardware is typically used.
Another problem with standoff acquisition is that the high resolution required in biometric applications leads to a “soda straw” problem in which peripheral information about the scene is lost. Therefore, it is necessary to simultaneously use low-resolution, wide field of view sensors in order to time the acquisition of a single high-resolution image containing usable biometric information. Thus, as the conditions become more challenging, the cost of standoff biometric acquisition increases due to the many additional pieces of hardware that are required.